Welcome to LPC

• Olivier Lopez

Room: G. Iltis (LPC) and Seminar room maison d'hôtes (GANIL)

Welcome from organisers Room: G. Iltis (LPC) and Seminar room maison d'hôtes (GANIL)

Table ronde

• Adnan Ghribi ({CNRS}UPR3266)
• Sabine Crépé-Renaudin (CNRS/IN2P3/LPSC)

Room: G. Iltis - La problématique du calcul et des données - La connexion entre les centres de calculs - La stratégie nationale et européenne

Introduction from GANIL director

• Fanny Farget (GANIL)
• Fanny FARGET ({CNRS}INS1618)

Room: Maison d'hôtes

IDEFICS: A Framework for Accelerating Deep Learning Adoption in Industry (Other)

• Tom FRANCOIS (CTA, LAPP, IN2P3)

Room: Maison d'hôtes The rapid advancements in deep learning, particularly in computer vision, present significant opportunities for industrial innovation. However, many companies face substantial barriers to entry, including the high cost of computational resources and a shortage of specialized machine learning talent. The IDEFICS project aims to address these challenges by providing a comprehensive framework that offers both state-of-the-art computational infrastructure and the expertise of skilled machine learning engineers and researchers. This initiative serves as a catalyst for businesses to integrate advanced AI technologies into their operations without prohibitive upfront investment. This presentation will first outline the global vision and operational model of the IDEFICS project, detailing how we collaborate with companies to identify and execute research and development projects. We will then delve into a specific case study: an object detection project developed for an industrial partner. This section will cover the practical implementation of a solution using YOLOv7, and some of the directions we decided to explore. **Keywords:** Deep Learning, Computer Vision, Object Detection, YOLOv7, Technology Transfer, Industrial AI

From Prototyping to Production: Integrating and Scaling GNN Tracking for the HL-LHC within the ATLAS Software Framework (Object detection and reconstruction)

• Sylvain Caillou (L2I Toulouse, CNRS/IN2P3, Université de Toulouse)

Room: G. Iltis (LPC) and Seminar room maison d'hôtes (GANIL) From Prototyping to Production: Integrating and Scaling GNN Tracking for the HL-LHC within the ATLAS Software Framework The High-Luminosity LHC (HL-LHC) upgrade of the ATLAS Inner Tracker (ITk) presents unprecedented challenges for track reconstruction, driven by the large number of silicon cluster readouts and the high throughput required under tight computing constraints. Graph Neural Networks (GNNs) have emerged as a promising solution to address these challenges [1–3], delivering competitive reconstruction performance at sub-second inference time. This contribution presents recent progress in deploying the GNN-based tracking pipeline within the ATLAS software framework [4]. Advances include inference optimizations (mixed precision, model reduction, compilation technologies) - as well as the implementation of dedicated GPU kernels to accelerate graph building and segmentation. We will present the first studies of end-to-end throughput in the ATLAS production environment, highlighting the integration of the GNN pipeline into existing workflows, its robustness under realistic detector conditions, and its ability to scale to the data volumes expected at the HL-LHC. These results demonstrate the maturity of the approach and its readiness for large-scale deployment in preparation for HL-LHC data taking. [1] C. Rougier et al., Towards a realistic track reconstruction algorithm based on Graph Neural Networks for the HL-LHC, CHEP 2021, https://doi.org/10.1051/epjconf/202125103047 [2] C. Rougier et al., ATLAS ITk Track Reconstruction with a GNN-based Pipeline, CTD 2022, https://doi.org/10.5281/zenodo.8119864 [3] A. Lazar et al., Improving Computational Performance of ATLAS GNN Track Reconstruction Pipeline, to appear in CHEP 2024 proceedings [4] B. Huth et al., Expected physics and computing performance of the ATLAS ITk GNN-based Track Reconstruction Chain, CTD 2025, available at https://indico.cern.ch/event/1499357/contributions/6621928/